A Theoretical Framework for Evaluating Psychiatric Research Strategies.

Kentaro Katahira, Yuichi Yamashita
{"title":"A Theoretical Framework for Evaluating Psychiatric Research Strategies.","authors":"Kentaro Katahira,&nbsp;Yuichi Yamashita","doi":"10.1162/CPSY_a_00008","DOIUrl":null,"url":null,"abstract":"<p><p>One of the major goals of basic studies in psychiatry is to find etiological mechanisms or biomarkers of mental disorders. A standard research strategy to pursue this goal is to compare observations of potential factors from patients with those from healthy controls. Classifications of individuals into patient and control groups are generally based on a diagnostic system, such as the <i>Diagnostic and Statistical Manual of Mental Disorders (DSM)</i> or the <i>International Classification of Diseases</i> (<i>ICD</i>). Several flaws in these conventional diagnostic-based approaches have been recognized. The flaws are primarily due to the complexity in the relation between the pathogenetic factors (causes) and disorders: The current diagnostic categories may not reflect the underlying etiological mechanisms. To overcome this difficulty, the National Institute of Mental Health initiated a novel research strategy called Research Domain Criteria (RDoC), which encourages studies to focus on the neurobiological mechanisms and core aspects of behavior rather than to rely on traditional diagnostic categories. However, how RDoC can improve research in psychiatry remains a matter of debate. In this article, we propose a theoretical framework for evaluating psychiatric research strategies, including the conventional diagnostic category-based approaches and the RDoC approach. The proposed framework is based on the statistical modeling of the processes of how the disorder arises from pathogenetic factors. This framework provides the statistical power to quantify how likely relevant pathogenetic factors are to be detected under various research strategies. On the basis of the proposed framework, we can discuss which approach performs better in different types of situations. We present several theoretical and numerical results that highlight the advantages and disadvantages of the strategies. We also demonstrate how a computational model is incorporated into the proposed framework as a generative model of behavioral observations. This demonstration highlights how the computational models contribute to designing psychiatric studies.</p>","PeriodicalId":72664,"journal":{"name":"Computational psychiatry (Cambridge, Mass.)","volume":"1 ","pages":"184-207"},"PeriodicalIF":0.0000,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1162/CPSY_a_00008","citationCount":"15","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computational psychiatry (Cambridge, Mass.)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1162/CPSY_a_00008","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 15

Abstract

One of the major goals of basic studies in psychiatry is to find etiological mechanisms or biomarkers of mental disorders. A standard research strategy to pursue this goal is to compare observations of potential factors from patients with those from healthy controls. Classifications of individuals into patient and control groups are generally based on a diagnostic system, such as the Diagnostic and Statistical Manual of Mental Disorders (DSM) or the International Classification of Diseases (ICD). Several flaws in these conventional diagnostic-based approaches have been recognized. The flaws are primarily due to the complexity in the relation between the pathogenetic factors (causes) and disorders: The current diagnostic categories may not reflect the underlying etiological mechanisms. To overcome this difficulty, the National Institute of Mental Health initiated a novel research strategy called Research Domain Criteria (RDoC), which encourages studies to focus on the neurobiological mechanisms and core aspects of behavior rather than to rely on traditional diagnostic categories. However, how RDoC can improve research in psychiatry remains a matter of debate. In this article, we propose a theoretical framework for evaluating psychiatric research strategies, including the conventional diagnostic category-based approaches and the RDoC approach. The proposed framework is based on the statistical modeling of the processes of how the disorder arises from pathogenetic factors. This framework provides the statistical power to quantify how likely relevant pathogenetic factors are to be detected under various research strategies. On the basis of the proposed framework, we can discuss which approach performs better in different types of situations. We present several theoretical and numerical results that highlight the advantages and disadvantages of the strategies. We also demonstrate how a computational model is incorporated into the proposed framework as a generative model of behavioral observations. This demonstration highlights how the computational models contribute to designing psychiatric studies.

Abstract Image

Abstract Image

Abstract Image

评估精神病学研究策略的理论框架。
精神病学基础研究的主要目标之一是寻找精神障碍的病因机制或生物标志物。追求这一目标的标准研究策略是比较患者和健康对照组对潜在因素的观察结果。将个体分为患者组和对照组通常基于诊断系统,例如精神障碍诊断和统计手册(DSM)或国际疾病分类(ICD)。人们已经认识到这些传统的基于诊断的方法存在一些缺陷。这些缺陷主要是由于致病因素(原因)和疾病之间关系的复杂性:目前的诊断类别可能无法反映潜在的病因机制。为了克服这一困难,美国国家心理健康研究所启动了一项名为“研究领域标准”(RDoC)的新研究策略,鼓励研究关注神经生物学机制和行为的核心方面,而不是依赖传统的诊断类别。然而,RDoC如何改进精神病学研究仍然是一个有争议的问题。在这篇文章中,我们提出了一个评估精神病学研究策略的理论框架,包括传统的基于诊断类别的方法和RDoC方法。所提出的框架是基于对疾病如何由致病因素引起的过程的统计建模。该框架提供了统计能力来量化在各种研究策略下检测到相关致病因素的可能性。在所提出的框架的基础上,我们可以讨论哪种方法在不同类型的情况下表现更好。我们给出了几个理论和数值结果,强调了这些策略的优点和缺点。我们还演示了如何将计算模型作为行为观察的生成模型纳入所提出的框架中。这一演示突出了计算模型如何有助于设计精神病学研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
4.30
自引率
0.00%
发文量
0
审稿时长
17 weeks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信